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Mind over Mortar: Examining IEQ Scores and Financial Services Companies Performance

Mind over Mortar: Examining IEQ Scores and Financial Services Companies Performance JOURNAL OF SUSTAINABLE REAL ESTATE 2022, VOL. 14, NO. 1, 42–56 ARES https://doi.org/10.1080/19498276.2022.2102624 American Real Estate Society Mind over Mortar: Examining IEQ Scores and Financial Services Companies Performance a,b Saul Nurick Urban Real Estate Research Unit, Department of Construction Economics and Management, University of Cape Town, Cape Town, South Africa; Department of Psychology, School of Human & Community Development, University of the Witwatersrand, Johannesburg, South Africa KEYWORDS ABSTRACT Average green return ratio This paper investigates green buildings and organizational performance, using financial serv- (AGRRi); financial services ices companies (FSCs) located in green and non-green buildings. Returns of low, moderate, companies (FSCs); indoor and high-risk investment products were used to underpin organizational performance. FSCs environmental quality (IEQ); based in green buildings on average outperformed their competitors in non-green build- performance ings. One statistically significant relationship (high-risk fund) was found when assessing returns and IEQ. Average green return ratios (AGRRi) determined the discount/premium of the incremental return per IEQ point of a FSC based in green building(s). However, there were individual FSCs located in non-green buildings that outperformed some of the FSCs based in green buildings. Introduction organization, which either performs (or not), based on pre-determined industry defined metrics This research is underpinned by the proposition of (Akimoto et al., 2010; Biron et al., 2006; Chadburn Nurick and Thatcher (2021) that green building fea- et al., 2017; Fisk et al., 2011; Wyon, 2004). tures and initiatives (GBFIs), specifically enhanced indoor environmental quality (IEQ), result in increased individual productivity and organizational Rationale performance. There is still much conjecture amongst The emergence of green building councils started to previous researchers regarding the notion that appear in the late 1990s. One of the mandates of green buildings result in increased productivity, these councils was to develop a tool to measure/ although this outcome is often claimed by the vari- assess potential green certified buildings. This ous green building councils. Multiple researchers required a set of standard categories within green have debated and struggled to fully establish the building tools, which included electricity, water, man- relationship between green buildings and individual agement, transport, materials, land use, emissions, productivity, and ultimately enhanced organizational innovation, and internal environmental quality (IEQ) performance (Byrd et al., 2016; Feige et al., 2013; (Green Building Council of South Africa (GBCSA), 2015; Rasheed & Byrd, 2017; Thatcher & Milner, 2016). United Kingdom Green Building Council (UKGBC), Some of the methods that have focused on prod- 2015; United States Green Building Council (USGBC), uctivity have applied several approaches, which 2015). The GBCSA uses Green Star South Africa as the include inter alia building user surveys (BUS), post- tool to measure office buildings, where a building is occupancy evaluations (POE), longitudinal studies, considered green certified if it achieves four, five, or and simulated office experiments, usually in labora- tories. All these approaches have usually focused on six stars (Green Building Council of South Africa the actual knowledge worker versus the overall (GBCSA), 2015). The benefits of green building, more CONTACT Saul Nurick sd.nurick@uct.ac.za Urban Real Estate Research Unit, Department of Construction Economics and Management, University of Cape Town, South Africa. 2022 The Author(s). Published with license by Taylor & Francis Group, LLC This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF SUSTAINABLE REAL ESTATE 43 specifically GBFIs, have been demonstrated from a that focuses on the organization, as shown in building operations perspective, in term of utility sav- Figure 1. The focal point of this paper is the link ings, and how this benefits both the owner and the between individual productivity and organizational tenant. Measuring utility savings is relatively simple performance, i.e., cross-over of the dotted line. The and therefore it is easy to establish if an individual or reason for highlighting this linkage is due to green group of GBFIs results in a long-term financial benefit. building advocates stating, albeit with mixed sup- Assessing individual productivity levels of building porting empirical evidence (MacNaughton et al., occupants is more challenging and linking individual 2015; Park & Yoon, 2011; Tanabe et al., 2015; Zhang productivity levels to organizational performance adds et al., 2010), that green buildings result in increased a further layer of complexity. Attempting to link GBFIs productivity. Assuming that this is the case, then (specifically enhanced IEQ) to organizational perform- increased productivity should equate to enhanced ance has to date not been fully established within the organizational performance. If it could be estab- research community. There have been some research- lished that organizational performance was to be ers (Byrd et al., 2016) that have debated the accuracy enhanced because of superior IEQ, then this would of measuring productivity and these inaccuracies have benefit both the building owner (reducing the asset falsely been attributed to the notion that enhanced risk) and tenant, which would therefore ultimately IEQ results in improved productivity. Furthermore, a justify the implementation of GBFIs (or at least an literature review conducted by Rasheed and Byrd investment in IEQ features). The model shows that (2017) indicated that self-evaluation is not an accurate that the performance level also is influenced by approach with regards to productivity in an office leadership and the economy (Prouska et al., 2016; environment within the context of IEQ. Feige et al. Saleem et al., 2019) and is partially assessed by (2013) indicated that improved occupant comfort, as absenteeism and turnover. Ultimately organizational a result of enhanced IEQ can reduce the turnover rate performance level is measured by financial gain of an organization, which may have a positive impact (increased ROI), that if linked to enhanced IEQ, on the financial performance of the organization. would justify the implementation of specific GBFIs, However, Feige et al. (2013) were unable to fully con- as this would also add value to the building from firm a link between occupant comfort and productiv- an owner/occupier perspective. ity and were unable to provide evidence that if While there has been previous research (Akimoto et al., 2010; Alker et al., 2014; Clements-Croome & occupants were more productive then this would lead to financial gains for the organization. It should Baizhan, 2000; Feige et al., 2013; Garnys & Wargocki be noted that much of the literature examining office 2015; Heerwagen, 2000;Mallawaarachchi et al., 2016; occupants within the context of IEQ use the term 2017; Miller et al., 2009; Newsham et al., 2017;Singh “performance” in relation to the individual occupant et al., 2010; Thatcher & Milner, 2014;Wyon, 2004) (i.e., individual productivity as it is defined in this that has focused on the relationship between green paper) and not in relation to the overall organization. buildings and individual productivity, there has yet The research indicates that there is currently very little to be research that links green certified office build- literature that specifically attempts to establish a rela- ings, more specifically enhanced IEQ, to overall tionship between GBFIs (i.e., enhanced IEQ) and organizational performance. The reason for a lack of organizational performance. Another indicating factor research that specifically focuses on organizational is that past researchers have been unable to link indi- performance in the context of GBFIs is that develop- vidual productivity to organizational performance ing a robust generic performance measurement (Feige et al., 2013;Flamholtz, 2009;Kampschroer framework for knowledge-based workers has been et al., 2007), which further highlights the difficulty of difficult to develop and be fully accepted by both exhibiting direct links of enhanced IEQ to individual industry and academia. Office-based companies productivity through to improved organizational (knowledge-based/white collar workers) vary depend- performance. ing on the profession and industry. For example, The rationale underpinning this research is based measuring organizational performance in the legal, on the model developed by Nurick and Thatcher engineering, and banking sectors would have to (2021), specifically the section below the dotted line done using very different underlying data and tools, 44 S. NURICK and would therefore be a challenge to normalize duration of the workday, as access to secure financial and compare. Therefore, a new approach is required systems and confidential data normally occurs within to isolate organizational performance data that can the confines of the office building and other secure be potentially linked to the indoor environment. One firewalls. Additionally, unlike other professions (e.g., of the challenges in a knowledge-based environment engineers, auditors, and lawyers) that are often required to leave the office as part of their work to is the ability to link certain professional outcomes in a financially quantifiable manner. This research uses meet clients (Figure 1). a bottom-up approach where the researcher inten- tionally focused on financial services companies Method and Hypotheses (FSCs), as these types of organizations are assessed Method on their ability to outperform the market, but more importantly to outperform their competitors, in terms The overarching method is a bottom-up approach of annualized returns. Furthermore, knowledge-based comprising three phases where twenty FSCs offering workers employed by FSCs are office bound for the three commonly provided investments (i.e., an Figure 1. Linking GBFIs to individual productivity and organizational performance (Nurick & Thatcher, 2021, p. 29). JOURNAL OF SUSTAINABLE REAL ESTATE 45 income fund, a balanced fund, and a South African Hypotheses equity fund) were selected. These three funds are For each of the three fund types the asset class offered by all the FSCs included in this study and allocation was similar across all the FSCs. The can be directly linked to individual productivity, as annualized returns from inception for each of highly effective (productive) asset managers find these investment funds across each of the FSCs investment opportunities that outperform compet- was included, assuming the investment fund was ing FSCs. Financial services tenants are intentionally established at least five years ago in order to selected as their inputs and outputs are quantifiable dilute theimpact of COVID-19 onthe investment and therefore comparable. markets. It was also assumed that financial services The first phase involved comparing ten South analysts were all exposed to the same market African based FSCs (1–10) located across nineteen information and fluctuations regardless of the green certified buildings with ten FSCs (11–20) state of the market. Using the annualized return located in thirteen non-green buildings. The nine- for each investment fund for each FSC, the aver- teen green buildings were all owned by the largest age return and standard deviation was calculated South African real estate investment trust (REIT) that for both the green and non-green building groups. own the majority of green certified office buildings The average return was used to forecast the pro- in South Africa. These FSCs 1–10 were the only busi- jection of R100 over thirty years in order to deter- nesses in this REITs portfolio that offer the three mine if there was a significant difference in the main funds (income, balanced, and South African average of FSCs located in green buildings versus equity). The other ten FSCs were owned by various non-green buildings. property-owning companies in South Africa that also offered the same three funds. Not all FSCs Hypothesis 1. FSCs located in green buildings in operating in the South African market have all three the long-term on average outperform competing funds with the focus mainly on balanced and South companies located in non-green buildings. African equity funds, while ignoring low risk/low The second phase attempted to establish if there return funds, i.e., income funds. The FSCs ranged was a relationship between enhanced IEQ and from institutional investors, insurance companies, annualized returns. The annualized return since asset management companies and boutique private inception were compared to the annualized return equity investment firms. Sixteen buildings were 4- since taking occupation of the green building – five star green certified, while two buildings were 5-star years (approximate average for FSCs 1–10). The dif- and one building was 6-star certified. Desktop ana- ference in return – discount/premium (delta – incep- lysis of publicly available data were obtained from tion/5 year returns) was calculated and then ranking downloaded fact sheets of the three funds from FSC applied to FSCs 1–10 for each of the three funds. It websites for income funds (low risk), balanced funds is expected that as the ranking worsens (tending (moderate risk), and South African equity funds towards 10), so the IEQ score would decrease. (high risk). All the FSCs were managed in-house by Pearson’s Correlation analysis was conducted to test a single or multiple asset manager(s). Additionally, this hypothesis, where a negative correlation would all the investment funds were active funds, i.e., not support the notion that there is a relationship funds of funds or index linked funds. A change in between IEQ and annualized returns. Length of time asset management personal was deemed accept- (interaction term) was considered as a co-variate in able over the different time periods, as all asset relation to annualized return for FSCs 1–10. managers make investment decisions based on the same market information and conditions at a given Hypothesis 2. There is a relative improvement (i.e., point in time. It is therefore assumed that an asset ranking improves) of annualized returns for FSCs manager (and their support staff) working in a when compared to the number of IEQ points for a green building with superior IEQ would outperform green building. an asset manager (and their support staff) working in a non-green building and this would be reflected Hypothesis 3. Time in a green building (operation- in the fund performance. alized as years since occupation) has a positive 46 S. NURICK impact on the relative improvement in annual- a ¼ Income Fund; b ¼ Balanced Fund; c ¼ SA ized returns. Equity Fund Average Return (NGBa;b;c) ¼ NGB average return The third phase comprised several calculations. The for each of the three investments (a;b;c) first calculation determined the difference between GRRi ¼ Green Return Ratio incremental (i) for FSC x the returns for each FSC within one of the invest- (a or b or c) ments(income,balanced or South African equity) and AGRRi ¼ FSC x is incrementally(i) generating on aver- the average return of the corresponding non-green age per building y% discount/premium to the building group. For example, in the income fund (low average returns of FSC 11–20. risk) data set, FSC 1 located in a green building has an annualized return since inception of 9%, and the For FSC 1 located in a green building, the AGRRi average returns for the income fund in all the non- was 0.18%. This means that on average FSC 1 incre- green buildings is 7.86%. The incremental return was mentally, but slightly, outperformed the control group 1.14%. FSC 1 was located across three buildings with (FSCs in non-green buildings). FSC 2 had higher incre- IEQ scores of 12, 13 and 19 points, respectively. The mental returns across all three investment options incremental return was divided by each IEQ score (to and a significantly higher AGRRi (1.48%). Therefore, normalize the output), resulting in a green return ratio the larger the difference of returns for investment incremental (GRRi) for each building for the income funds of FSCs located in green buildings compared to fund. The same process was repeated for the bal- the corresponding average return of all FSCs for a par- anced and South African equity investments, to calcu- ticular investment, the larger the AGRRi. late the GRRi for each building. The GRRi’sacross all the investments was summed and divided by the Hypothesis 4. Individual FSCs located in green number of buildings (3) housing FSC 1, resulting in an buildings generate superior fund returns compared average green return ratio incremental (AGRRi) per to the average returns of FSCs located in non-green building. The AGRRi gives an average indication per buildings when IEQ scores are normalized. building for eachFSC located ina greenbuilding(s) of the premium/discount expressed as an incremental Limitations return when compared to average returns against a sample of FSCs located in non-green buildings. This research is limited to the South African green The following formulae were applied: certified office market and FSCs that lease space in Return of FSC xðÞ GBa; b; c these buildings, where the FSCs must offer (amongst other financial products) the three main ðÞ Average Return NGBa; b; c funds (income, balanced, South African equity). Due ¼ Incremental Return FSC x ða; b; cÞ to the infancy of the green building movement in Incremental ReturnðÞ a; b; c South Africa, compared to developed real estate IEQ Points for Building x markets there is a relatively small sample of as-built ðÞ ¼ Green Return Ratios i a; b; c green certified office buildings. This results in a fur- ðÞ Green Return Ratios i ðÞ a þ Green Return Ratios i b þ Green Return Ratios i ðcÞ Total Number of Buildings for Fund x ðÞ ¼ Average Green Return Ratio per Building incremental ðÞ y% Where: ther reduced number of FSCs leasing space in green certified office buildings, which offer the three main FSC x ¼ Any FSC 1–10 funds (income, balanced, South African equity) GB ¼ Green Building included in the analysis. Furthermore, as stated in NGB ¼ Non-Green Building the introduction, other office tenants would not be JOURNAL OF SUSTAINABLE REAL ESTATE 47 Table 1. FSC and building breakdown. Green building number FSC Number of employees Stars Green star points IEQ points Years in a green building 1 1 ± 1,200 4 48 12 4 21 4 47 13 3 31 6 79 19 7 18 2 ± 50 4 45 5.5 5 4 3 ± 31,000 4 51 4.5 3 5 3 4 53 6.5 4 63 4 53 5 6 73 4 50 12 4 8 3 4 46 10 4 9 4 ± 8,000 5 73 17 4 10 5 ± 1,000 4 48 6.5 4 11 6 ± 25 4 50 4.5 5 12 7 ± 1,000 4 45 8.5 4 8 8 ± 400 4 46 10 4 13 9 ± 37,000 4 49 11.5 4 14 9 4 50 7.5 4 15 9 4 54 11 4 16 9 4 45 7 7 17 9 5 64 14 6 8 9 4 46 10 4 19 10 ± 220 4 46 11.5 3 FSC 3, FSC 8 and FSC 9 share Building No. 8. suitable for this study, as it difficult to provide a that are considered more vulnerable to infrastruc- more standardized quantification of their organiza- ture failures. This illustrates the main reason for tional performance in relation to their physical office being in a green certified office building (regardless of industry), as green buildings can have an oper- environment, especially if their employees spend time away from the office to conduct business. It ational and therefore financial impact on should be further noted that other factors such as their tenants. the demographic profile (specifically age), political profile (conservative versus liberal/progressive), cor- Results porate governance, and access to resources of office Green Certified Buildings vs. Non-Green Buildings tenants were not considered in this study. This is because these are factors that are luxuries within Annualized returns of ten FSCs located in nineteen the context of the South African office market and green certified buildings across the three types of are more applicable to developed economies and funds were compared to the same return data of ten property markets. There is demand for South FSCs located in thirteen non-green buildings. The African green office buildings, due to the unpredict- FSCs ranged from approximately 25–37,000 employ- ability and increasing prices of utilities. Electricity ees, where some FSCs were located across multiple (which the tenant normally pays) has been increas- buildings and others were based in a single multi- ing at above inflationary levels. Added to these tenanted building. Table 1 provides a breakdown of increased costs, South Africa has, for at least the the buildings for each of the FSCs in terms of green last ten years, experienced load shedding (sched- certification (Green Star South Africa), IEQ points, and uled power black outs). Rental for office green the period of time (years) that each FSC had been buildings is not charged at a premium due to the located in a green building. Table 1 contains sixteen relatively weak office market and macro economy in four (45–59 points), two five (60–74 points) and one the last five to seven years. Therefore, in South six (75–100 points) star green certified buildings, Africa green certified office buildings provide a form respectively. There is not necessarily a direct link to of protection (future proofing of business opera- points scored for IEQ and overall points awarded for tions) against failures in infrastructure and rising the building. For example, building 1 has an IEQ costs in the form of renewable energy sources and/ score of 12 and a total building score of 48, while or the use of natural light through efficient design, building 4 has an IEQ score of 4.5 and a total build- i.e., there is not a premium for green buildings, but ing score of 51, where both buildings are four star rather a brown discount for non-green buildings green certified. This is because there are several 48 S. NURICK Table 2. FSC (non-green buildings) breakdown. Income Fund Projecon - Compounded Annually Non-green building number FSC Number of employees R1,200 R1,081 R1,100 1 11 ± 725 R1,000 2 12 ± 250 R968 R900 R800 R700 5 13 ± 170 R600 614 ±10 R500 715 ±25 R400 816 ±25 R300 9 17 ± 260 R200 10 18 ± 25 R100 11 18 0 5 10 15 20 25 30 Years 12 19 ± 25 Green Buildings Non-Green Buildings 13 20 ± 335 Figure 2. Income fund projection – compounded annually. contributing factors to an IEQ score with varying monthly for thirty years, then this results in FSCs points allocations. The IEQ score in the Green Star located in green buildings yielding a FV ¼ R1,081 rating tool includes the following aspects: indoor air (annually) and FV ¼ 1,181 (monthly), and the FSCs quality; lighting comfort; thermal comfort; acoustic located in non-green buildings generating quality; and daylight and views. While IEQ tends to FV ¼ R968 (annually) and FV (monthly) ¼ R1,049. focus on building occupants, there are also other cat- This means that the difference between the annu- egories within the Green Star South Africa rating tool ally and monthly compounded FV is 11.75 and that carry more points (e.g., electricity and water) 12.66%, respectively. Figures 2 and 3 provide a and focus directly on the operational capabilities of graphical representation of these projections. the building (Tables 1 and 2). Table 2 provides a breakdown for the FSCs Balanced Fund (Moderate Risk) (11–20) located in thirteen non-green buildings. The A balance income fund is considered a moderate number of employees ranges from approxi- risk investment because it typically diversifies across mately 10–725. a range of asset classes with varying degrees of risk. This normally comprises a combination of equities Income Fund (Low Risk) (approximately 70% of the fund, with the remaining An income fund typically comprises interest bearing 30% including property, commodities, bonds, and investments that include South African government money market/bank deposits). A small proportion of bonds, cash investments through several local the fund (approximately 30%) can be invested off- banks, and possibly individual/corporate money shore. The benchmark is similar funds in the market market accounts. Income funds tend to use con- and is compared to the market value weighted sumer price index (CPI), i.e., inflation, as a bench- return of funds in the South African multi-asset high mark, and therefore attempt to outperform equity category. this benchmark. The ten FSCs located in green buildings indicated Of the ten FSCs located in green buildings, FSC 3 that FSC 1 had the highest annualized return had the highest annualized return since inception (15.4%) and FSC 10 had the lowest return (8%). Five (11%), while FSC 7 had the lowest return (3.8%). companies (FSC 1–15.4%, FSC 3–12%, FSC FSC 1 (9%), FSC 2 (9.14%), FSC 3 (11%), FSC 6 5–12.45%, FSC 7–13.9%, and FSC 8–12.3%) all had (8.7%), FSC 8 (9.2%) and FSC 9 (9.64%) all yielded returns greater than the average return for FSCs 11 returns greater than the average return for FSCs to 20 (x ¼ 10.62%). The standard deviation for FSCs 11–20 (x ¼ 7.86%). The average return for FSCs 1–10 1–10 and FSCs 11–20 are S ¼ 2.17% and S ¼ 3.10%, is x ¼ 8.26%, and the standard deviation for the respectively. The thirty-year compounded annual FSCs located in green buildings and non-green and monthly FV’s of R100 for FSCs 1–10, using the buildings is S ¼ 1.85% and S ¼ 1.16%, respectively. average return (x ¼ 11.37%) is R2,530 and R2,981, When the average returns (x ¼ 8.26%, x ¼ 7.86%) for respectively. The corresponding FV’s for FSCs 11-20, the two groups of FSCs is used to project the future applying the average return (x ¼ 10.62%) are R2,068 value (FV) of R100, compounded both annually and and R2,388, as shown in Figures 4 and 5. The JOURNAL OF SUSTAINABLE REAL ESTATE 49 Income Fund Projecon - Compounded Monthly Balanced Fund Projecon - Compounded R1,181 Monthly R1,200 R1,100 R3,100 R1,049 R1,000 R2,981 R900 R2,600 R800 R2,388 R700 R2,100 R600 R500 R1,600 R400 R300 R1,100 R200 R100 R600 0 5 10 15 20 25 30 Years Green Buildings Non-Green Buildings R100 0 5 10 15 20 25 30 Years Figure 3. Income fund projection – compounded monthly. Green Buildings Non-Green Buildings Figure 5. Balanced fund projection – compounded monthly. Balanced Fund Projecon - Compounded were S ¼ 4.03% and S ¼ 4.21%, respectively. When Annually R100 was compounded annually and monthly for thirty years for FSCs 1–10 using the average return R3,100 (x ¼ 10.64%), this resulted in FV’s of R2,078 and R2,600 R2,530 R2,402, respectively. When the same calculation is R2,100 R2,068 conducted for FSCs 11–20, applying the average R1,600 return (x ¼ 7.3%) then the FV compounded annually R1,100 is R828 and compounded monthly is R888. The per- R600 centage difference between FSCs 1–10 (green build- R100 0 5 10 15 20 25 30 ing) and FSC 11–20 (non-green building) is 151% Years Green Buildings Non-Green Buildings (compounded annually) and 170% (compounded Figure 4. Balanced fund projection – compounded annually. monthly), as shown in Figures 6 and 7. difference between the green building and non- IEQ Scores Compared to Return green building groups is 22.34% (compounded annually) and 24.83% (compounded monthly). Correlation analyses were calculated to determine if there were any relationships between IEQ scores South African Equity Fund (High Risk) and annualized return in terms of rank after deter- A South African equity fund is considered a high- mining the annualized return delta for each invest- risk investment and aims to outperform the equity ment vehicle (FSCs 1–10) since inception when market over the long-term. A fund of this nature compared to the five-year annualized returns. For typically comprises at least 90% listed equities, with example, for FSC 1 for the income fund, the annual- the remaining 10% including cash and property ized return for five years and since inception were investments. A maximum of 40% of the assets can 10 and 9%, respectively, resulting in a premium of be listed outside of South Africa. This type of fund 1%. For income and balanced investments, the cor- is normally benchmarked against the Financial relation coefficients were r¼0.06 and r ¼ 0.14, Times Securities Exchange (FTSE) and/or the respectively. The South African equity investment Johannesburg Securities Exchange (JSE) All yielded a negative correlation of r¼0.76 Share Index. (p< 0.01), as shown in Figure 8, while the consoli- The South African Equity fund results indicate dated correlation (all three funds) was negative, but that for FSCs 1–10, the highest and lowest returns also not statistically significant, at r¼0.48. Based were obtained by FSC 7 (16%) and FSC 1 (3.9%), on these results there was no significant relation- respectively. Only three companies (FSC 1, FSC 4 ship between IEQ score and annualized return in and FSC 10) were below the average returns of terms of rank, except for South African equity funds. FSCs 11–20 (x ¼ 7.3%). The standard deviations for Further analysis of comparing annualized return the green building and non-green building groups (without implementing a ranking metric) to IEQ 50 S. NURICK scores indicated there was a sweet spot where FSCs that very low IEQ scores do not add value to located in building(s) with mid-range IEQ scores annualized returns, while very high IEQ scores do (approximately 7.5–10) tend to yield the best not result in a sufficient increase in annualized returns, as shown in Figure 9 (income fund), Figure returns. The extrapolation of the IEQ scores of 10 (balanced fund), and Figure 11 (South African 7.5–10 exhibited some commonalities, which equity fund). This result may provide an indication included points for indoor air quality testing, ther- mal comfort, lighting comfort, conducting an occu- pant comfort survey, an acoustic audit, and access South African Equity Fund Projecon - Compounded Annually to daylight. R2,600 Further analysis was conducted by applying a covariate (time spent in a green building) against R2,100 R2,078 annualized return. Due to the average time spent in R1,600 a green building for FSCs 1–10 was approximately five years, the annualized returns for the last five R1,100 R828 years were compared to an interaction term. The R600 interaction term is the product of the time spent in R100 a green building and the number of IEQ points. For 0 5 10 15 20 25 30 Years the income (r ¼ 0.06) and balanced funds (r ¼ 0.28) Green Buildings Non-Green Buildings there was no correlation between return and the Figure 6. SA equity fund projection – compounded annually. interaction term. There was a moderate negative correlation (r¼0.59, p< 0.1) for the South African South African Equity Fund Projecon - equity fund. As the interaction term increased the Compounded Monthly annualized return decreased, however the FSCs with R2,600 R2,402 IEQ scores between approximately 7.5 and 10 R2,100 yielded the best annualized returns. This could fur- R1,600 ther support the notion that there is a sweet spot for IEQ scores in relation to annualized return. R1,100 R888 R600 Average Green Return Ratio (AGRRi) R100 0 5 10 15 20 25 30 The AGRRi provides an average indication per build- Years Green Buildings Non-Green Buildings ing for each FSC located in a green building of the Figure 7. SA equity fund projection – compounded monthly. premium/discount expressed as an incremental SA Equity Fund Rank (Delta - Incepon/5 Year Returns) vs IEQ Points 18.0 17.0 16.0 14.7 14.0 11.5 12.0 10.2 10.0 10.0 8.5 7.6 8.0 6.5 5.5 6.0 4.5 4.0 2.0 0.0 1 2 345 6 789 10 FSC 4 FSC 1 FSC 10 FSC 2 FSC 3 FSC 9 FSC 7 FSC 8 FSC 5 FSC 6 Ranking and FSC Figure 8. South African equity fund rank (delta – inception/5 year returns) vs. IEQ Score. IEQ Score JOURNAL OF SUSTAINABLE REAL ESTATE 51 Income Fund Return vs IEQ Score 18.0 12.00% 17.0 11.00% 16.0 14.7 10.00% 9.64% 14.0 9.20% 9.14% 9.00% 8.70% 11.5 12.0 8.00% 10.2 7.41% 7.45% 7.25% 10.0 10.0 8.5 6.00% 7.6 8.0 6.5 5.5 6.0 4.00% 3.80% 4.5 4.0 2.00% 2.0 0.0 0.00% FSC 6 FSC 2 FSC 5 FSC 3 FSC 7 FSC 8 FSC 9 FSC 10 FSC 1 FSC 4 IEQ Points Return Linear (Return) Figure 9. Income fund return vs. IEQ Score. Balanced Fund Return vs IEQ Score 18.0 18.00% 17.0 16.0 16.00% 14.7 15.40% 14.0 14.00% 13.90% 12.45% 11.5 12.30% 12.0 12.00% 12.00% 10.2 11.07% 10.0 10.0 10.00% 9.90% 9.57% 8.5 9.11% 7.6 8.0 8.00% 8.00% 6.5 5.5 6.0 6.00% 4.5 4.0 4.00% 2.0 2.00% 0.0 0.00% FSC 6 FSC 2 FSC 5 FSC 3 FSC 7 FSC 8 FSC 9 FSC 10 FSC 1 FSC 4 IEQ Points Return Linear (Return) Figure 10. Balanced fund return vs. IEQ Score. return when compared to average returns against a FSC 1 was in three buildings where the GRRi for sample of FSCs located in non-green buildings. IEQ the income, balanced, and SA equity investments was score per building is embedded in this calculation 1.14, 4.78 and -3.4%, respectively. The sum of the in order to normalize the green return ratio (GRRi) GRRi’s was 0.54%. This results in an AGRRi of 0.18% for each building in relation to the incremental (0.54% divided by 3). Table 2 provides a numerical return for each investment vehicle for a particular breakdown for FSCs 1–10. FSC 4 and FSC 10 have FSC located in a green building. Nineteen IEQ scores negative AGRRi’s of -0.14 and -0.34% respectively, (i.e., one per building) were used to calculate the while the other eight FSCs have a positive AGRRi. The AGRRi for FSCs 1-10. AGRRi for FSC 1–10 was 0.46%, which indicates that 52 S. NURICK South African Equity Fund Return vs IEQ Score 18.0 18.00% 17.0 16.0 16.00% 16.00% 14.7 14.89% 14.65% 14.0 14.00% 13.80% 12.50% 11.5 12.0 12.00% 10.2 10.0 10.0 10.00% 9.60% 8.5 7.6 8.0 8.00% 7.72% 6.5 6.87% 6.50% 5.5 6.0 6.00% 4.5 4.0 4.00% 3.90% 2.0 2.00% 0.0 0.00% FSC 6 FSC 2 FSC 5 FSC 3 FSC 7 FSC 8 FSC 9 FSC 10 FSC 1 FSC 4 IEQ Points Return Linear (Return) Figure 11. South African equity fund return vs. IEQ Score. Hypothesis 2. There is a relative improvement of as a group FSCs 1–10 marginally outperformed the annualized returns for FSCs when compared to the non-green building group (FSCs 11–20) when an number of IEQ points for a green building. incremental return calculation is conducted taking into consideration IEQ scores (Table 3). The data indicated that there was no correlation between IEQ scores and annualized return for two Discussion of the three investment funds. A return discount/ The following four hypotheses were stated in the premium was calculated comparing annualized methods section: returns since inception to annualized returns for the last five years. This was followed by ranking FSCs Hypothesis 1. FSCs located in green buildings in 1–10 and mapping them to their corresponding IEQ the long-term on average outperform competing scores. The income and balanced funds did not companies located in non-green buildings. exhibit any correlation; however, the South African The results of the data analysis indicated that as a equity fund indicated a statistically significant nega- group FSCs 1–10 (green buildings) outperformed, tive correlation (r ¼ -0.76) at p< 0.01. This meant on average, FSCs 11–20 (non-green buildings) in that as the annualized return premium decreased so terms on annualized return since inception. There the ranking increased (worsened/tended to 10), or were, however, individual FSCs in the non-green in other words FSCs with the highest annualized building that outperformed FSCs in the green build- return premium exhibited the highest IEQ scores. ing group. A calculation was conducted using the Figure 8 provides a graphical illustration of the rela- average returns for the green and non-green build- tionship between IEQ scores and rank for the South ing groups to forecast R100 for thirty years, com- African equity fund. pounded both annually and monthly. For all three Hypothesis 3. Time in a green building has a posi- investment funds there was a substantial percent- tive impact on annualized returns. age difference in nominal future values. These dif- ferences increased as the risk factor of each fund Correlation analysis between the interaction term increased, i.e., the income fund had the smallest and annualized return for the last five years did not percentage difference, while the South African indicate a relationship between these two variables equity fund has the largest percentage difference. for the income and balanced funds. For the South JOURNAL OF SUSTAINABLE REAL ESTATE 53 Table 3. Average green return ratio (AGRRi) breakdown. GRRi GRRi – – balanced SA equity GRRi – income Incremental Incremental Incremental Incremental Incremental Return return to Incremental return to AGRRiTotal Total per return return to IEQ Balanced IEQ Return SA IEQ GRRi building Building No FSC IEQ points Income (%) points (%) (%) points (%) equity (%) points (%) Total (%) (%) 1 1 12 1.14 0.10 4.78 0.40 3.40 0.28 0.54 0.18 2 1 13 0.09 0.37 0.26 3 1 19 0.06 0.25 0.18 18 2 5.5 1.28 0.23 10.5 0.19 0.42 0.08 0.12 0.12 4 3 4.5 3.14 0.70 1.38 0.31 5.20 1.16 7.38 1.48 5 3 6.5 0.48 0.21 0.80 6 3 5 0.63 0.28 1.04 7 3 12 0.26 0.11 0.43 8 3 10 0.31 0.14 0.52 94 17 0.41 0.02 1.51 0.09 0.43 0.03 0.14 0.14 10 5 6.5 0.61 0.09 1.83 0.28 7.35 1.13 1.32 1.32 11 6 4.5 0.84 0.19 0.72 0.16 6.50 1.44 1.47 1.47 12 7 8.5 4.06 0.48 3.28 0.39 8.70 1.02 0.93 0.93 8 8 10 1.34 0.13 1.68 0.17 2.30 0.23 0.53 0.53 13 9 11.5 0.15 0.04 0.66 14 9 7.5 0.24 0.06 1.01 15 9 11 1.78 0.16 0.45 0.04 7.59 0.69 6.14 1.02 16 9 7 0.25 0.06 1.08 17 9 14 0.13 0.03 0.54 8 9 10 0.18 0.04 0.76 19 10 11.5 0.45 0.04 2.62 0.23 0.80 0.07 0.34 0.34 Average 9.8 0.40% 0.04 0.75 0.08 3.34 0.34 0.46 0.46 FSCs 3, 8 and 9 share Building No. 8. African equity fund there was a moderate negative and adjusted for the three investment funds held by correlation (r¼0.58) at p< 0.1, however this was each FSC. only statistically significant at p ¼ 0.10. Due to one The data indicated that there could be a high- fund providing statistically significant results at a level argument that FSCs located in green buildings relatively high p-value there an indication that there do, on average, outperform (based on annualized may be an IEQ sweet spot (7.5–10 points). An IEQ return) similar companies based in non-green build- score lower or higher than this range does not add ings. It could not be conclusively established that value in terms of annualized returns. there was a significant relationship between annual- ized return and IEQ scores, both from a ranking and Hypothesis 4. Individual FSCs located in green a nominal perspective to IEQ scores, despite the sig- buildings generate superior fund returns compared nificant finding in support of this relationship for to the average returns of FSCs located in non-green the high-risk South African Equity Fund. buildings when IEQ scores are normalized. Furthermore, using time spent in a green building The application of a new indicator, the average green as a covariate did not uncover a significant relation- return ratio (incremental) (AGRRi) was used to provide ship between annualized return and IEQ scores. This insight into the incremental increase in return for confirms that the indication of a significant relation- each FSC 1-10 against the average return of FSCs ship between enhanced IEQ and organizational per- 11–20 for each of the three investment funds. IEQ formance is difficult to establish as was stated by scores were used to normalize incremental calcula- Kampschroer et al. (2007); Flamholtz (2009); Feige tions. Eight of the FSCs located in green buildings et al. (2013), but is contrary to many green building indicated a marginally positive AGGRi, while the advocates (Alker et al., 2014). remaining two FSCs had slightly negative AGGRi’s. The A granular analysis of applying the AGRRi equations AGRRi’sfor each FSC1–10 were mostly positive, thus of return data across the three investment vehicles of providing further support to hypothesis 1 that individ- each FSC 1–10 compared to the average return of ual FSCs located in green buildings outperformed a FSCs 11–20 provided a different insight into the incre- group of FSCs located in non-green buildings. mental return of FSCs located in green buildings. Furthermore, this approach incorporated IEQ scores Eight of the FSCs located in green buildings produced 54 S. NURICK a small incremental return per IEQ point when com- of the pandemic. However, examples of internal fac- pared to the average return of the companies located tors could be investment policies set by executive in non-green buildings. Two FSCs performed relatively leadership (Saleem et al., 2019) at an organizational worsewhen a comparisonwas made to the sample level, which may restrict individual asset manager(s) of FSCs in the non-green building group. This new from fully applying their best judgement in terms of approach of assessing FSCs located in green and non- investment decisions. External factors tend to have green buildings has not been conducted by previous a longer lasting impact on returns as it generally researchers and therefore provides a different lens takes time for an economy to recover or for a polit- within a bottom-up approach of assessing organiza- ical system to change that will benefit the invest- tional performance within the context of GBFIs, specif- ment community. Furthermore, external factors are ically enhanced IEQ. The AGRRi method adds value to more difficult to manage than internal factors and assessing organizational performance within the finan- therefore they can have a greater residual impact cial services sectors in terms of enhanced IEQ, which on an organization in terms of strategy develop- is underpinned by green building certification. ment in order to pivot appropriately. These factors Analyzing organizational performance within the can distort annualized returns and make it difficult context of green building is a challenge as there are to establish if there is a relationship between IEQ factors that can influence both the productivity of scores and annualized return. the human resources and the organization’s per- The obvious limitation of this study is that it only formance as a whole (Prouska et al., 2016; Saleem focuses on FSCs and therefore cannot be general- et al., 2019). FSCs provide a relatively “clean” output ized for other office-based industries, or for that that can be compared against industry benchmarks matter other FSCs that do not offer the three com- and competing organizations. monly available investment funds included in this Green buildings in South Africa are classified as study. These three funds covered a range of invest- P-grade (premium) buildings, while the FSCs (11–20) ment risk profiles that aided the standardization of located in non-green buildings tend to be classified organizational performance of the FSCs located in as A-grade buildings, which typically also include green and non-green buildings. This study included high quality finishes and therefore command rela- a relatively small sample of South African FSCs tively high rental. There is a possibility that many A- located in green buildings. However, the sample of grade buildings do have some GBFIs (including FSCs was relatively complete as all the companies those related to IEQ), but the property owners renting space in South Africa’s largest REIT that choose not to opt for green certification, as this were in their green building portfolio and operating would be an additional cost that is not deemed in financial services were screened and only ten necessary. This could partially explain why there FSCs were found to have income, balanced and were individual occurrences of FSCs in the non- South African equity funds that were established at green buildings outperforming FSCs located in least five years ago. It should be further noted that green buildings, as the difference in the actual IEQ due to the small and exhausted sample size it is not of some A-grade and P-grade buildings is often not possible to determine and/or control for staff attrac- immediately noticeable. tion and retention (salaries and company culture) However, it must be noted that returns are sig- working for institutional versus boutique FSCs. nificantly impacted by a number of other external Another factor that is difficult to determine are and internal factors. External factors are characteris- other reasons (outside of protection against failing tics that are not in the control of asset manager(s), infrastructure) for FSCs to lease space in green certi- while internal factors are characteristics that are in fied office buildings. There could be a range of rea- the control of the asset manager(s) and the organ- sons, that may include corporate brand ization. Examples of external factors include unfore- enhancement, subtle recruiting strategy in the form seen socio-political and economic events that result a superior indoor working environment, and envir- in a sudden market shock (Prouska et al., 2016), such as COVID-19 where global economies were onmental consciousness as part of FSCs core com- reduced to only essential services during the height pany values, inter alia. JOURNAL OF SUSTAINABLE REAL ESTATE 55 Conclusion productivity. This data could be used to substantiate the findings of this research and forms part of the The study attempted to determine if there was any model (Figure 1) developed by (Nurick & Thatcher, benefits for FSCs located in a green building in terms 2021) to determine if there is a link between of organizational performance, as measured by annual- enhanced individual productivity and organizational ized return (Hypothesis 1 – partially accepted). There performance within the context of green office build- were indicationsthatFSCslocated in green buildings ings. Furthermore, this research could be replicated didon average yieldhigher annualizedreturns to FSCs in other markets where there is a larger sample of located in non-green buildings. The difference in per- green office buildings and FSCs that offer similar formance is not always immediately large but can lead funds, that may have different investment challenges to a large difference when allowed to compound over to that of South Africa. Additionally, a larger sample time, thus benefiting both the FSCs and their clients would allow for an investigation into FSC perform- over the long-term. It should be noted that extrapolat- ance in relation to company size (number of employ- ing future values is a hypothetical exercise, which is ees), and create the possibility of analyzing other often performed in industry by FSCs with the assump- funds that are commonly offered in developed finan- tion that current market conditions are relatively sta- cial markets, which are not widely offered to South ble/predictable over a long period of time. It could not African investors. A similar research approach could be conclusively established that there was a relation- also be applied to retail property in green buildings, ship between annualized return and IEQ scores where the financial performance of similar tenants (Hypothesis 2 – rejected), however there was an indi- (e.g., anchor tenants such as major supermarkets) cation that there is a moderate IEQ score range where could be compared to non-green certified retail annualized return seems to be maximized relative to buildings. There is also potential for the application the lower returns in the sample. Furthermore, the of similar type hypotheses within the context of light amount of time in a green building did not provide industrial property (i.e., distribution centres) to deter- any insight into the organizational performance of mine if an e-commerce type tenant performs better FSCs located in green buildings (Hypothesis 3 – in a green certified industrial building. rejected). A new metric (AGRRi) was used to normalize the IEQ score to determine if there was an incremental Acknowledgments difference in annualized return for each FSCs located The author wishes to acknowledge and thank Professor in a green building when compared to the average Andrew Thatcher at the University of the Witwatersrand for returns of the FSCs located in non-green buildings his assistance with this paper, specifically regarding the stat- (Hypothesis 4 – partially accepted). The AGRRi showed istical components and proof reading. that generally FSCs located in green buildings slightly outperformed FSCs located in non-green buildings. Disclosure statement However, it should also be noted that individual FSCs No potential conflict of interest was reported by the author(s). located in non-green buildings did outperform some FSCs in the green building group. This study addressed four hypotheses that focused on organizational per- Data availability statement formance rather than individual productivity within the The data is available at this link. context of green building, which yielded mixed results. This was an important step as it provides further References insight into whether green buildings, more specifically enhanced IEQ impact the bottom line of FSCs that Akimoto, T., Tanabe, S-i., Yanai, T., & Sasaki, M. (2010). Thermal comfort and productivity - Evaluation of work- operateinthis space. place environment in a task conditioned office. 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Mind over Mortar: Examining IEQ Scores and Financial Services Companies Performance

Journal of Sustainable Real Estate , Volume 14 (1): 15 – Dec 31, 2022

Mind over Mortar: Examining IEQ Scores and Financial Services Companies Performance

Abstract

Abstract This paper investigates green buildings and organizational performance, using financial services companies (FSCs) located in green and non-green buildings. Returns of low, moderate, and high-risk investment products were used to underpin organizational performance. FSCs based in green buildings on average outperformed their competitors in non-green buildings. One statistically significant relationship (high-risk fund) was found when assessing returns and IEQ. Average green return...
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© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC
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1949-8284
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10.1080/19498276.2022.2102624
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Abstract

JOURNAL OF SUSTAINABLE REAL ESTATE 2022, VOL. 14, NO. 1, 42–56 ARES https://doi.org/10.1080/19498276.2022.2102624 American Real Estate Society Mind over Mortar: Examining IEQ Scores and Financial Services Companies Performance a,b Saul Nurick Urban Real Estate Research Unit, Department of Construction Economics and Management, University of Cape Town, Cape Town, South Africa; Department of Psychology, School of Human & Community Development, University of the Witwatersrand, Johannesburg, South Africa KEYWORDS ABSTRACT Average green return ratio This paper investigates green buildings and organizational performance, using financial serv- (AGRRi); financial services ices companies (FSCs) located in green and non-green buildings. Returns of low, moderate, companies (FSCs); indoor and high-risk investment products were used to underpin organizational performance. FSCs environmental quality (IEQ); based in green buildings on average outperformed their competitors in non-green build- performance ings. One statistically significant relationship (high-risk fund) was found when assessing returns and IEQ. Average green return ratios (AGRRi) determined the discount/premium of the incremental return per IEQ point of a FSC based in green building(s). However, there were individual FSCs located in non-green buildings that outperformed some of the FSCs based in green buildings. Introduction organization, which either performs (or not), based on pre-determined industry defined metrics This research is underpinned by the proposition of (Akimoto et al., 2010; Biron et al., 2006; Chadburn Nurick and Thatcher (2021) that green building fea- et al., 2017; Fisk et al., 2011; Wyon, 2004). tures and initiatives (GBFIs), specifically enhanced indoor environmental quality (IEQ), result in increased individual productivity and organizational Rationale performance. There is still much conjecture amongst The emergence of green building councils started to previous researchers regarding the notion that appear in the late 1990s. One of the mandates of green buildings result in increased productivity, these councils was to develop a tool to measure/ although this outcome is often claimed by the vari- assess potential green certified buildings. This ous green building councils. Multiple researchers required a set of standard categories within green have debated and struggled to fully establish the building tools, which included electricity, water, man- relationship between green buildings and individual agement, transport, materials, land use, emissions, productivity, and ultimately enhanced organizational innovation, and internal environmental quality (IEQ) performance (Byrd et al., 2016; Feige et al., 2013; (Green Building Council of South Africa (GBCSA), 2015; Rasheed & Byrd, 2017; Thatcher & Milner, 2016). United Kingdom Green Building Council (UKGBC), Some of the methods that have focused on prod- 2015; United States Green Building Council (USGBC), uctivity have applied several approaches, which 2015). The GBCSA uses Green Star South Africa as the include inter alia building user surveys (BUS), post- tool to measure office buildings, where a building is occupancy evaluations (POE), longitudinal studies, considered green certified if it achieves four, five, or and simulated office experiments, usually in labora- tories. All these approaches have usually focused on six stars (Green Building Council of South Africa the actual knowledge worker versus the overall (GBCSA), 2015). The benefits of green building, more CONTACT Saul Nurick sd.nurick@uct.ac.za Urban Real Estate Research Unit, Department of Construction Economics and Management, University of Cape Town, South Africa. 2022 The Author(s). Published with license by Taylor & Francis Group, LLC This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF SUSTAINABLE REAL ESTATE 43 specifically GBFIs, have been demonstrated from a that focuses on the organization, as shown in building operations perspective, in term of utility sav- Figure 1. The focal point of this paper is the link ings, and how this benefits both the owner and the between individual productivity and organizational tenant. Measuring utility savings is relatively simple performance, i.e., cross-over of the dotted line. The and therefore it is easy to establish if an individual or reason for highlighting this linkage is due to green group of GBFIs results in a long-term financial benefit. building advocates stating, albeit with mixed sup- Assessing individual productivity levels of building porting empirical evidence (MacNaughton et al., occupants is more challenging and linking individual 2015; Park & Yoon, 2011; Tanabe et al., 2015; Zhang productivity levels to organizational performance adds et al., 2010), that green buildings result in increased a further layer of complexity. Attempting to link GBFIs productivity. Assuming that this is the case, then (specifically enhanced IEQ) to organizational perform- increased productivity should equate to enhanced ance has to date not been fully established within the organizational performance. If it could be estab- research community. There have been some research- lished that organizational performance was to be ers (Byrd et al., 2016) that have debated the accuracy enhanced because of superior IEQ, then this would of measuring productivity and these inaccuracies have benefit both the building owner (reducing the asset falsely been attributed to the notion that enhanced risk) and tenant, which would therefore ultimately IEQ results in improved productivity. Furthermore, a justify the implementation of GBFIs (or at least an literature review conducted by Rasheed and Byrd investment in IEQ features). The model shows that (2017) indicated that self-evaluation is not an accurate that the performance level also is influenced by approach with regards to productivity in an office leadership and the economy (Prouska et al., 2016; environment within the context of IEQ. Feige et al. Saleem et al., 2019) and is partially assessed by (2013) indicated that improved occupant comfort, as absenteeism and turnover. Ultimately organizational a result of enhanced IEQ can reduce the turnover rate performance level is measured by financial gain of an organization, which may have a positive impact (increased ROI), that if linked to enhanced IEQ, on the financial performance of the organization. would justify the implementation of specific GBFIs, However, Feige et al. (2013) were unable to fully con- as this would also add value to the building from firm a link between occupant comfort and productiv- an owner/occupier perspective. ity and were unable to provide evidence that if While there has been previous research (Akimoto et al., 2010; Alker et al., 2014; Clements-Croome & occupants were more productive then this would lead to financial gains for the organization. It should Baizhan, 2000; Feige et al., 2013; Garnys & Wargocki be noted that much of the literature examining office 2015; Heerwagen, 2000;Mallawaarachchi et al., 2016; occupants within the context of IEQ use the term 2017; Miller et al., 2009; Newsham et al., 2017;Singh “performance” in relation to the individual occupant et al., 2010; Thatcher & Milner, 2014;Wyon, 2004) (i.e., individual productivity as it is defined in this that has focused on the relationship between green paper) and not in relation to the overall organization. buildings and individual productivity, there has yet The research indicates that there is currently very little to be research that links green certified office build- literature that specifically attempts to establish a rela- ings, more specifically enhanced IEQ, to overall tionship between GBFIs (i.e., enhanced IEQ) and organizational performance. The reason for a lack of organizational performance. Another indicating factor research that specifically focuses on organizational is that past researchers have been unable to link indi- performance in the context of GBFIs is that develop- vidual productivity to organizational performance ing a robust generic performance measurement (Feige et al., 2013;Flamholtz, 2009;Kampschroer framework for knowledge-based workers has been et al., 2007), which further highlights the difficulty of difficult to develop and be fully accepted by both exhibiting direct links of enhanced IEQ to individual industry and academia. Office-based companies productivity through to improved organizational (knowledge-based/white collar workers) vary depend- performance. ing on the profession and industry. For example, The rationale underpinning this research is based measuring organizational performance in the legal, on the model developed by Nurick and Thatcher engineering, and banking sectors would have to (2021), specifically the section below the dotted line done using very different underlying data and tools, 44 S. NURICK and would therefore be a challenge to normalize duration of the workday, as access to secure financial and compare. Therefore, a new approach is required systems and confidential data normally occurs within to isolate organizational performance data that can the confines of the office building and other secure be potentially linked to the indoor environment. One firewalls. Additionally, unlike other professions (e.g., of the challenges in a knowledge-based environment engineers, auditors, and lawyers) that are often required to leave the office as part of their work to is the ability to link certain professional outcomes in a financially quantifiable manner. This research uses meet clients (Figure 1). a bottom-up approach where the researcher inten- tionally focused on financial services companies Method and Hypotheses (FSCs), as these types of organizations are assessed Method on their ability to outperform the market, but more importantly to outperform their competitors, in terms The overarching method is a bottom-up approach of annualized returns. Furthermore, knowledge-based comprising three phases where twenty FSCs offering workers employed by FSCs are office bound for the three commonly provided investments (i.e., an Figure 1. Linking GBFIs to individual productivity and organizational performance (Nurick & Thatcher, 2021, p. 29). JOURNAL OF SUSTAINABLE REAL ESTATE 45 income fund, a balanced fund, and a South African Hypotheses equity fund) were selected. These three funds are For each of the three fund types the asset class offered by all the FSCs included in this study and allocation was similar across all the FSCs. The can be directly linked to individual productivity, as annualized returns from inception for each of highly effective (productive) asset managers find these investment funds across each of the FSCs investment opportunities that outperform compet- was included, assuming the investment fund was ing FSCs. Financial services tenants are intentionally established at least five years ago in order to selected as their inputs and outputs are quantifiable dilute theimpact of COVID-19 onthe investment and therefore comparable. markets. It was also assumed that financial services The first phase involved comparing ten South analysts were all exposed to the same market African based FSCs (1–10) located across nineteen information and fluctuations regardless of the green certified buildings with ten FSCs (11–20) state of the market. Using the annualized return located in thirteen non-green buildings. The nine- for each investment fund for each FSC, the aver- teen green buildings were all owned by the largest age return and standard deviation was calculated South African real estate investment trust (REIT) that for both the green and non-green building groups. own the majority of green certified office buildings The average return was used to forecast the pro- in South Africa. These FSCs 1–10 were the only busi- jection of R100 over thirty years in order to deter- nesses in this REITs portfolio that offer the three mine if there was a significant difference in the main funds (income, balanced, and South African average of FSCs located in green buildings versus equity). The other ten FSCs were owned by various non-green buildings. property-owning companies in South Africa that also offered the same three funds. Not all FSCs Hypothesis 1. FSCs located in green buildings in operating in the South African market have all three the long-term on average outperform competing funds with the focus mainly on balanced and South companies located in non-green buildings. African equity funds, while ignoring low risk/low The second phase attempted to establish if there return funds, i.e., income funds. The FSCs ranged was a relationship between enhanced IEQ and from institutional investors, insurance companies, annualized returns. The annualized return since asset management companies and boutique private inception were compared to the annualized return equity investment firms. Sixteen buildings were 4- since taking occupation of the green building – five star green certified, while two buildings were 5-star years (approximate average for FSCs 1–10). The dif- and one building was 6-star certified. Desktop ana- ference in return – discount/premium (delta – incep- lysis of publicly available data were obtained from tion/5 year returns) was calculated and then ranking downloaded fact sheets of the three funds from FSC applied to FSCs 1–10 for each of the three funds. It websites for income funds (low risk), balanced funds is expected that as the ranking worsens (tending (moderate risk), and South African equity funds towards 10), so the IEQ score would decrease. (high risk). All the FSCs were managed in-house by Pearson’s Correlation analysis was conducted to test a single or multiple asset manager(s). Additionally, this hypothesis, where a negative correlation would all the investment funds were active funds, i.e., not support the notion that there is a relationship funds of funds or index linked funds. A change in between IEQ and annualized returns. Length of time asset management personal was deemed accept- (interaction term) was considered as a co-variate in able over the different time periods, as all asset relation to annualized return for FSCs 1–10. managers make investment decisions based on the same market information and conditions at a given Hypothesis 2. There is a relative improvement (i.e., point in time. It is therefore assumed that an asset ranking improves) of annualized returns for FSCs manager (and their support staff) working in a when compared to the number of IEQ points for a green building with superior IEQ would outperform green building. an asset manager (and their support staff) working in a non-green building and this would be reflected Hypothesis 3. Time in a green building (operation- in the fund performance. alized as years since occupation) has a positive 46 S. NURICK impact on the relative improvement in annual- a ¼ Income Fund; b ¼ Balanced Fund; c ¼ SA ized returns. Equity Fund Average Return (NGBa;b;c) ¼ NGB average return The third phase comprised several calculations. The for each of the three investments (a;b;c) first calculation determined the difference between GRRi ¼ Green Return Ratio incremental (i) for FSC x the returns for each FSC within one of the invest- (a or b or c) ments(income,balanced or South African equity) and AGRRi ¼ FSC x is incrementally(i) generating on aver- the average return of the corresponding non-green age per building y% discount/premium to the building group. For example, in the income fund (low average returns of FSC 11–20. risk) data set, FSC 1 located in a green building has an annualized return since inception of 9%, and the For FSC 1 located in a green building, the AGRRi average returns for the income fund in all the non- was 0.18%. This means that on average FSC 1 incre- green buildings is 7.86%. The incremental return was mentally, but slightly, outperformed the control group 1.14%. FSC 1 was located across three buildings with (FSCs in non-green buildings). FSC 2 had higher incre- IEQ scores of 12, 13 and 19 points, respectively. The mental returns across all three investment options incremental return was divided by each IEQ score (to and a significantly higher AGRRi (1.48%). Therefore, normalize the output), resulting in a green return ratio the larger the difference of returns for investment incremental (GRRi) for each building for the income funds of FSCs located in green buildings compared to fund. The same process was repeated for the bal- the corresponding average return of all FSCs for a par- anced and South African equity investments, to calcu- ticular investment, the larger the AGRRi. late the GRRi for each building. The GRRi’sacross all the investments was summed and divided by the Hypothesis 4. Individual FSCs located in green number of buildings (3) housing FSC 1, resulting in an buildings generate superior fund returns compared average green return ratio incremental (AGRRi) per to the average returns of FSCs located in non-green building. The AGRRi gives an average indication per buildings when IEQ scores are normalized. building for eachFSC located ina greenbuilding(s) of the premium/discount expressed as an incremental Limitations return when compared to average returns against a sample of FSCs located in non-green buildings. This research is limited to the South African green The following formulae were applied: certified office market and FSCs that lease space in Return of FSC xðÞ GBa; b; c these buildings, where the FSCs must offer (amongst other financial products) the three main ðÞ Average Return NGBa; b; c funds (income, balanced, South African equity). Due ¼ Incremental Return FSC x ða; b; cÞ to the infancy of the green building movement in Incremental ReturnðÞ a; b; c South Africa, compared to developed real estate IEQ Points for Building x markets there is a relatively small sample of as-built ðÞ ¼ Green Return Ratios i a; b; c green certified office buildings. This results in a fur- ðÞ Green Return Ratios i ðÞ a þ Green Return Ratios i b þ Green Return Ratios i ðcÞ Total Number of Buildings for Fund x ðÞ ¼ Average Green Return Ratio per Building incremental ðÞ y% Where: ther reduced number of FSCs leasing space in green certified office buildings, which offer the three main FSC x ¼ Any FSC 1–10 funds (income, balanced, South African equity) GB ¼ Green Building included in the analysis. Furthermore, as stated in NGB ¼ Non-Green Building the introduction, other office tenants would not be JOURNAL OF SUSTAINABLE REAL ESTATE 47 Table 1. FSC and building breakdown. Green building number FSC Number of employees Stars Green star points IEQ points Years in a green building 1 1 ± 1,200 4 48 12 4 21 4 47 13 3 31 6 79 19 7 18 2 ± 50 4 45 5.5 5 4 3 ± 31,000 4 51 4.5 3 5 3 4 53 6.5 4 63 4 53 5 6 73 4 50 12 4 8 3 4 46 10 4 9 4 ± 8,000 5 73 17 4 10 5 ± 1,000 4 48 6.5 4 11 6 ± 25 4 50 4.5 5 12 7 ± 1,000 4 45 8.5 4 8 8 ± 400 4 46 10 4 13 9 ± 37,000 4 49 11.5 4 14 9 4 50 7.5 4 15 9 4 54 11 4 16 9 4 45 7 7 17 9 5 64 14 6 8 9 4 46 10 4 19 10 ± 220 4 46 11.5 3 FSC 3, FSC 8 and FSC 9 share Building No. 8. suitable for this study, as it difficult to provide a that are considered more vulnerable to infrastruc- more standardized quantification of their organiza- ture failures. This illustrates the main reason for tional performance in relation to their physical office being in a green certified office building (regardless of industry), as green buildings can have an oper- environment, especially if their employees spend time away from the office to conduct business. It ational and therefore financial impact on should be further noted that other factors such as their tenants. the demographic profile (specifically age), political profile (conservative versus liberal/progressive), cor- Results porate governance, and access to resources of office Green Certified Buildings vs. Non-Green Buildings tenants were not considered in this study. This is because these are factors that are luxuries within Annualized returns of ten FSCs located in nineteen the context of the South African office market and green certified buildings across the three types of are more applicable to developed economies and funds were compared to the same return data of ten property markets. There is demand for South FSCs located in thirteen non-green buildings. The African green office buildings, due to the unpredict- FSCs ranged from approximately 25–37,000 employ- ability and increasing prices of utilities. Electricity ees, where some FSCs were located across multiple (which the tenant normally pays) has been increas- buildings and others were based in a single multi- ing at above inflationary levels. Added to these tenanted building. Table 1 provides a breakdown of increased costs, South Africa has, for at least the the buildings for each of the FSCs in terms of green last ten years, experienced load shedding (sched- certification (Green Star South Africa), IEQ points, and uled power black outs). Rental for office green the period of time (years) that each FSC had been buildings is not charged at a premium due to the located in a green building. Table 1 contains sixteen relatively weak office market and macro economy in four (45–59 points), two five (60–74 points) and one the last five to seven years. Therefore, in South six (75–100 points) star green certified buildings, Africa green certified office buildings provide a form respectively. There is not necessarily a direct link to of protection (future proofing of business opera- points scored for IEQ and overall points awarded for tions) against failures in infrastructure and rising the building. For example, building 1 has an IEQ costs in the form of renewable energy sources and/ score of 12 and a total building score of 48, while or the use of natural light through efficient design, building 4 has an IEQ score of 4.5 and a total build- i.e., there is not a premium for green buildings, but ing score of 51, where both buildings are four star rather a brown discount for non-green buildings green certified. This is because there are several 48 S. NURICK Table 2. FSC (non-green buildings) breakdown. Income Fund Projecon - Compounded Annually Non-green building number FSC Number of employees R1,200 R1,081 R1,100 1 11 ± 725 R1,000 2 12 ± 250 R968 R900 R800 R700 5 13 ± 170 R600 614 ±10 R500 715 ±25 R400 816 ±25 R300 9 17 ± 260 R200 10 18 ± 25 R100 11 18 0 5 10 15 20 25 30 Years 12 19 ± 25 Green Buildings Non-Green Buildings 13 20 ± 335 Figure 2. Income fund projection – compounded annually. contributing factors to an IEQ score with varying monthly for thirty years, then this results in FSCs points allocations. The IEQ score in the Green Star located in green buildings yielding a FV ¼ R1,081 rating tool includes the following aspects: indoor air (annually) and FV ¼ 1,181 (monthly), and the FSCs quality; lighting comfort; thermal comfort; acoustic located in non-green buildings generating quality; and daylight and views. While IEQ tends to FV ¼ R968 (annually) and FV (monthly) ¼ R1,049. focus on building occupants, there are also other cat- This means that the difference between the annu- egories within the Green Star South Africa rating tool ally and monthly compounded FV is 11.75 and that carry more points (e.g., electricity and water) 12.66%, respectively. Figures 2 and 3 provide a and focus directly on the operational capabilities of graphical representation of these projections. the building (Tables 1 and 2). Table 2 provides a breakdown for the FSCs Balanced Fund (Moderate Risk) (11–20) located in thirteen non-green buildings. The A balance income fund is considered a moderate number of employees ranges from approxi- risk investment because it typically diversifies across mately 10–725. a range of asset classes with varying degrees of risk. This normally comprises a combination of equities Income Fund (Low Risk) (approximately 70% of the fund, with the remaining An income fund typically comprises interest bearing 30% including property, commodities, bonds, and investments that include South African government money market/bank deposits). A small proportion of bonds, cash investments through several local the fund (approximately 30%) can be invested off- banks, and possibly individual/corporate money shore. The benchmark is similar funds in the market market accounts. Income funds tend to use con- and is compared to the market value weighted sumer price index (CPI), i.e., inflation, as a bench- return of funds in the South African multi-asset high mark, and therefore attempt to outperform equity category. this benchmark. The ten FSCs located in green buildings indicated Of the ten FSCs located in green buildings, FSC 3 that FSC 1 had the highest annualized return had the highest annualized return since inception (15.4%) and FSC 10 had the lowest return (8%). Five (11%), while FSC 7 had the lowest return (3.8%). companies (FSC 1–15.4%, FSC 3–12%, FSC FSC 1 (9%), FSC 2 (9.14%), FSC 3 (11%), FSC 6 5–12.45%, FSC 7–13.9%, and FSC 8–12.3%) all had (8.7%), FSC 8 (9.2%) and FSC 9 (9.64%) all yielded returns greater than the average return for FSCs 11 returns greater than the average return for FSCs to 20 (x ¼ 10.62%). The standard deviation for FSCs 11–20 (x ¼ 7.86%). The average return for FSCs 1–10 1–10 and FSCs 11–20 are S ¼ 2.17% and S ¼ 3.10%, is x ¼ 8.26%, and the standard deviation for the respectively. The thirty-year compounded annual FSCs located in green buildings and non-green and monthly FV’s of R100 for FSCs 1–10, using the buildings is S ¼ 1.85% and S ¼ 1.16%, respectively. average return (x ¼ 11.37%) is R2,530 and R2,981, When the average returns (x ¼ 8.26%, x ¼ 7.86%) for respectively. The corresponding FV’s for FSCs 11-20, the two groups of FSCs is used to project the future applying the average return (x ¼ 10.62%) are R2,068 value (FV) of R100, compounded both annually and and R2,388, as shown in Figures 4 and 5. The JOURNAL OF SUSTAINABLE REAL ESTATE 49 Income Fund Projecon - Compounded Monthly Balanced Fund Projecon - Compounded R1,181 Monthly R1,200 R1,100 R3,100 R1,049 R1,000 R2,981 R900 R2,600 R800 R2,388 R700 R2,100 R600 R500 R1,600 R400 R300 R1,100 R200 R100 R600 0 5 10 15 20 25 30 Years Green Buildings Non-Green Buildings R100 0 5 10 15 20 25 30 Years Figure 3. Income fund projection – compounded monthly. Green Buildings Non-Green Buildings Figure 5. Balanced fund projection – compounded monthly. Balanced Fund Projecon - Compounded were S ¼ 4.03% and S ¼ 4.21%, respectively. When Annually R100 was compounded annually and monthly for thirty years for FSCs 1–10 using the average return R3,100 (x ¼ 10.64%), this resulted in FV’s of R2,078 and R2,600 R2,530 R2,402, respectively. When the same calculation is R2,100 R2,068 conducted for FSCs 11–20, applying the average R1,600 return (x ¼ 7.3%) then the FV compounded annually R1,100 is R828 and compounded monthly is R888. The per- R600 centage difference between FSCs 1–10 (green build- R100 0 5 10 15 20 25 30 ing) and FSC 11–20 (non-green building) is 151% Years Green Buildings Non-Green Buildings (compounded annually) and 170% (compounded Figure 4. Balanced fund projection – compounded annually. monthly), as shown in Figures 6 and 7. difference between the green building and non- IEQ Scores Compared to Return green building groups is 22.34% (compounded annually) and 24.83% (compounded monthly). Correlation analyses were calculated to determine if there were any relationships between IEQ scores South African Equity Fund (High Risk) and annualized return in terms of rank after deter- A South African equity fund is considered a high- mining the annualized return delta for each invest- risk investment and aims to outperform the equity ment vehicle (FSCs 1–10) since inception when market over the long-term. A fund of this nature compared to the five-year annualized returns. For typically comprises at least 90% listed equities, with example, for FSC 1 for the income fund, the annual- the remaining 10% including cash and property ized return for five years and since inception were investments. A maximum of 40% of the assets can 10 and 9%, respectively, resulting in a premium of be listed outside of South Africa. This type of fund 1%. For income and balanced investments, the cor- is normally benchmarked against the Financial relation coefficients were r¼0.06 and r ¼ 0.14, Times Securities Exchange (FTSE) and/or the respectively. The South African equity investment Johannesburg Securities Exchange (JSE) All yielded a negative correlation of r¼0.76 Share Index. (p< 0.01), as shown in Figure 8, while the consoli- The South African Equity fund results indicate dated correlation (all three funds) was negative, but that for FSCs 1–10, the highest and lowest returns also not statistically significant, at r¼0.48. Based were obtained by FSC 7 (16%) and FSC 1 (3.9%), on these results there was no significant relation- respectively. Only three companies (FSC 1, FSC 4 ship between IEQ score and annualized return in and FSC 10) were below the average returns of terms of rank, except for South African equity funds. FSCs 11–20 (x ¼ 7.3%). The standard deviations for Further analysis of comparing annualized return the green building and non-green building groups (without implementing a ranking metric) to IEQ 50 S. NURICK scores indicated there was a sweet spot where FSCs that very low IEQ scores do not add value to located in building(s) with mid-range IEQ scores annualized returns, while very high IEQ scores do (approximately 7.5–10) tend to yield the best not result in a sufficient increase in annualized returns, as shown in Figure 9 (income fund), Figure returns. The extrapolation of the IEQ scores of 10 (balanced fund), and Figure 11 (South African 7.5–10 exhibited some commonalities, which equity fund). This result may provide an indication included points for indoor air quality testing, ther- mal comfort, lighting comfort, conducting an occu- pant comfort survey, an acoustic audit, and access South African Equity Fund Projecon - Compounded Annually to daylight. R2,600 Further analysis was conducted by applying a covariate (time spent in a green building) against R2,100 R2,078 annualized return. Due to the average time spent in R1,600 a green building for FSCs 1–10 was approximately five years, the annualized returns for the last five R1,100 R828 years were compared to an interaction term. The R600 interaction term is the product of the time spent in R100 a green building and the number of IEQ points. For 0 5 10 15 20 25 30 Years the income (r ¼ 0.06) and balanced funds (r ¼ 0.28) Green Buildings Non-Green Buildings there was no correlation between return and the Figure 6. SA equity fund projection – compounded annually. interaction term. There was a moderate negative correlation (r¼0.59, p< 0.1) for the South African South African Equity Fund Projecon - equity fund. As the interaction term increased the Compounded Monthly annualized return decreased, however the FSCs with R2,600 R2,402 IEQ scores between approximately 7.5 and 10 R2,100 yielded the best annualized returns. This could fur- R1,600 ther support the notion that there is a sweet spot for IEQ scores in relation to annualized return. R1,100 R888 R600 Average Green Return Ratio (AGRRi) R100 0 5 10 15 20 25 30 The AGRRi provides an average indication per build- Years Green Buildings Non-Green Buildings ing for each FSC located in a green building of the Figure 7. SA equity fund projection – compounded monthly. premium/discount expressed as an incremental SA Equity Fund Rank (Delta - Incepon/5 Year Returns) vs IEQ Points 18.0 17.0 16.0 14.7 14.0 11.5 12.0 10.2 10.0 10.0 8.5 7.6 8.0 6.5 5.5 6.0 4.5 4.0 2.0 0.0 1 2 345 6 789 10 FSC 4 FSC 1 FSC 10 FSC 2 FSC 3 FSC 9 FSC 7 FSC 8 FSC 5 FSC 6 Ranking and FSC Figure 8. South African equity fund rank (delta – inception/5 year returns) vs. IEQ Score. IEQ Score JOURNAL OF SUSTAINABLE REAL ESTATE 51 Income Fund Return vs IEQ Score 18.0 12.00% 17.0 11.00% 16.0 14.7 10.00% 9.64% 14.0 9.20% 9.14% 9.00% 8.70% 11.5 12.0 8.00% 10.2 7.41% 7.45% 7.25% 10.0 10.0 8.5 6.00% 7.6 8.0 6.5 5.5 6.0 4.00% 3.80% 4.5 4.0 2.00% 2.0 0.0 0.00% FSC 6 FSC 2 FSC 5 FSC 3 FSC 7 FSC 8 FSC 9 FSC 10 FSC 1 FSC 4 IEQ Points Return Linear (Return) Figure 9. Income fund return vs. IEQ Score. Balanced Fund Return vs IEQ Score 18.0 18.00% 17.0 16.0 16.00% 14.7 15.40% 14.0 14.00% 13.90% 12.45% 11.5 12.30% 12.0 12.00% 12.00% 10.2 11.07% 10.0 10.0 10.00% 9.90% 9.57% 8.5 9.11% 7.6 8.0 8.00% 8.00% 6.5 5.5 6.0 6.00% 4.5 4.0 4.00% 2.0 2.00% 0.0 0.00% FSC 6 FSC 2 FSC 5 FSC 3 FSC 7 FSC 8 FSC 9 FSC 10 FSC 1 FSC 4 IEQ Points Return Linear (Return) Figure 10. Balanced fund return vs. IEQ Score. return when compared to average returns against a FSC 1 was in three buildings where the GRRi for sample of FSCs located in non-green buildings. IEQ the income, balanced, and SA equity investments was score per building is embedded in this calculation 1.14, 4.78 and -3.4%, respectively. The sum of the in order to normalize the green return ratio (GRRi) GRRi’s was 0.54%. This results in an AGRRi of 0.18% for each building in relation to the incremental (0.54% divided by 3). Table 2 provides a numerical return for each investment vehicle for a particular breakdown for FSCs 1–10. FSC 4 and FSC 10 have FSC located in a green building. Nineteen IEQ scores negative AGRRi’s of -0.14 and -0.34% respectively, (i.e., one per building) were used to calculate the while the other eight FSCs have a positive AGRRi. The AGRRi for FSCs 1-10. AGRRi for FSC 1–10 was 0.46%, which indicates that 52 S. NURICK South African Equity Fund Return vs IEQ Score 18.0 18.00% 17.0 16.0 16.00% 16.00% 14.7 14.89% 14.65% 14.0 14.00% 13.80% 12.50% 11.5 12.0 12.00% 10.2 10.0 10.0 10.00% 9.60% 8.5 7.6 8.0 8.00% 7.72% 6.5 6.87% 6.50% 5.5 6.0 6.00% 4.5 4.0 4.00% 3.90% 2.0 2.00% 0.0 0.00% FSC 6 FSC 2 FSC 5 FSC 3 FSC 7 FSC 8 FSC 9 FSC 10 FSC 1 FSC 4 IEQ Points Return Linear (Return) Figure 11. South African equity fund return vs. IEQ Score. Hypothesis 2. There is a relative improvement of as a group FSCs 1–10 marginally outperformed the annualized returns for FSCs when compared to the non-green building group (FSCs 11–20) when an number of IEQ points for a green building. incremental return calculation is conducted taking into consideration IEQ scores (Table 3). The data indicated that there was no correlation between IEQ scores and annualized return for two Discussion of the three investment funds. A return discount/ The following four hypotheses were stated in the premium was calculated comparing annualized methods section: returns since inception to annualized returns for the last five years. This was followed by ranking FSCs Hypothesis 1. FSCs located in green buildings in 1–10 and mapping them to their corresponding IEQ the long-term on average outperform competing scores. The income and balanced funds did not companies located in non-green buildings. exhibit any correlation; however, the South African The results of the data analysis indicated that as a equity fund indicated a statistically significant nega- group FSCs 1–10 (green buildings) outperformed, tive correlation (r ¼ -0.76) at p< 0.01. This meant on average, FSCs 11–20 (non-green buildings) in that as the annualized return premium decreased so terms on annualized return since inception. There the ranking increased (worsened/tended to 10), or were, however, individual FSCs in the non-green in other words FSCs with the highest annualized building that outperformed FSCs in the green build- return premium exhibited the highest IEQ scores. ing group. A calculation was conducted using the Figure 8 provides a graphical illustration of the rela- average returns for the green and non-green build- tionship between IEQ scores and rank for the South ing groups to forecast R100 for thirty years, com- African equity fund. pounded both annually and monthly. For all three Hypothesis 3. Time in a green building has a posi- investment funds there was a substantial percent- tive impact on annualized returns. age difference in nominal future values. These dif- ferences increased as the risk factor of each fund Correlation analysis between the interaction term increased, i.e., the income fund had the smallest and annualized return for the last five years did not percentage difference, while the South African indicate a relationship between these two variables equity fund has the largest percentage difference. for the income and balanced funds. For the South JOURNAL OF SUSTAINABLE REAL ESTATE 53 Table 3. Average green return ratio (AGRRi) breakdown. GRRi GRRi – – balanced SA equity GRRi – income Incremental Incremental Incremental Incremental Incremental Return return to Incremental return to AGRRiTotal Total per return return to IEQ Balanced IEQ Return SA IEQ GRRi building Building No FSC IEQ points Income (%) points (%) (%) points (%) equity (%) points (%) Total (%) (%) 1 1 12 1.14 0.10 4.78 0.40 3.40 0.28 0.54 0.18 2 1 13 0.09 0.37 0.26 3 1 19 0.06 0.25 0.18 18 2 5.5 1.28 0.23 10.5 0.19 0.42 0.08 0.12 0.12 4 3 4.5 3.14 0.70 1.38 0.31 5.20 1.16 7.38 1.48 5 3 6.5 0.48 0.21 0.80 6 3 5 0.63 0.28 1.04 7 3 12 0.26 0.11 0.43 8 3 10 0.31 0.14 0.52 94 17 0.41 0.02 1.51 0.09 0.43 0.03 0.14 0.14 10 5 6.5 0.61 0.09 1.83 0.28 7.35 1.13 1.32 1.32 11 6 4.5 0.84 0.19 0.72 0.16 6.50 1.44 1.47 1.47 12 7 8.5 4.06 0.48 3.28 0.39 8.70 1.02 0.93 0.93 8 8 10 1.34 0.13 1.68 0.17 2.30 0.23 0.53 0.53 13 9 11.5 0.15 0.04 0.66 14 9 7.5 0.24 0.06 1.01 15 9 11 1.78 0.16 0.45 0.04 7.59 0.69 6.14 1.02 16 9 7 0.25 0.06 1.08 17 9 14 0.13 0.03 0.54 8 9 10 0.18 0.04 0.76 19 10 11.5 0.45 0.04 2.62 0.23 0.80 0.07 0.34 0.34 Average 9.8 0.40% 0.04 0.75 0.08 3.34 0.34 0.46 0.46 FSCs 3, 8 and 9 share Building No. 8. African equity fund there was a moderate negative and adjusted for the three investment funds held by correlation (r¼0.58) at p< 0.1, however this was each FSC. only statistically significant at p ¼ 0.10. Due to one The data indicated that there could be a high- fund providing statistically significant results at a level argument that FSCs located in green buildings relatively high p-value there an indication that there do, on average, outperform (based on annualized may be an IEQ sweet spot (7.5–10 points). An IEQ return) similar companies based in non-green build- score lower or higher than this range does not add ings. It could not be conclusively established that value in terms of annualized returns. there was a significant relationship between annual- ized return and IEQ scores, both from a ranking and Hypothesis 4. Individual FSCs located in green a nominal perspective to IEQ scores, despite the sig- buildings generate superior fund returns compared nificant finding in support of this relationship for to the average returns of FSCs located in non-green the high-risk South African Equity Fund. buildings when IEQ scores are normalized. Furthermore, using time spent in a green building The application of a new indicator, the average green as a covariate did not uncover a significant relation- return ratio (incremental) (AGRRi) was used to provide ship between annualized return and IEQ scores. This insight into the incremental increase in return for confirms that the indication of a significant relation- each FSC 1-10 against the average return of FSCs ship between enhanced IEQ and organizational per- 11–20 for each of the three investment funds. IEQ formance is difficult to establish as was stated by scores were used to normalize incremental calcula- Kampschroer et al. (2007); Flamholtz (2009); Feige tions. Eight of the FSCs located in green buildings et al. (2013), but is contrary to many green building indicated a marginally positive AGGRi, while the advocates (Alker et al., 2014). remaining two FSCs had slightly negative AGGRi’s. The A granular analysis of applying the AGRRi equations AGRRi’sfor each FSC1–10 were mostly positive, thus of return data across the three investment vehicles of providing further support to hypothesis 1 that individ- each FSC 1–10 compared to the average return of ual FSCs located in green buildings outperformed a FSCs 11–20 provided a different insight into the incre- group of FSCs located in non-green buildings. mental return of FSCs located in green buildings. Furthermore, this approach incorporated IEQ scores Eight of the FSCs located in green buildings produced 54 S. NURICK a small incremental return per IEQ point when com- of the pandemic. However, examples of internal fac- pared to the average return of the companies located tors could be investment policies set by executive in non-green buildings. Two FSCs performed relatively leadership (Saleem et al., 2019) at an organizational worsewhen a comparisonwas made to the sample level, which may restrict individual asset manager(s) of FSCs in the non-green building group. This new from fully applying their best judgement in terms of approach of assessing FSCs located in green and non- investment decisions. External factors tend to have green buildings has not been conducted by previous a longer lasting impact on returns as it generally researchers and therefore provides a different lens takes time for an economy to recover or for a polit- within a bottom-up approach of assessing organiza- ical system to change that will benefit the invest- tional performance within the context of GBFIs, specif- ment community. Furthermore, external factors are ically enhanced IEQ. The AGRRi method adds value to more difficult to manage than internal factors and assessing organizational performance within the finan- therefore they can have a greater residual impact cial services sectors in terms of enhanced IEQ, which on an organization in terms of strategy develop- is underpinned by green building certification. ment in order to pivot appropriately. These factors Analyzing organizational performance within the can distort annualized returns and make it difficult context of green building is a challenge as there are to establish if there is a relationship between IEQ factors that can influence both the productivity of scores and annualized return. the human resources and the organization’s per- The obvious limitation of this study is that it only formance as a whole (Prouska et al., 2016; Saleem focuses on FSCs and therefore cannot be general- et al., 2019). FSCs provide a relatively “clean” output ized for other office-based industries, or for that that can be compared against industry benchmarks matter other FSCs that do not offer the three com- and competing organizations. monly available investment funds included in this Green buildings in South Africa are classified as study. These three funds covered a range of invest- P-grade (premium) buildings, while the FSCs (11–20) ment risk profiles that aided the standardization of located in non-green buildings tend to be classified organizational performance of the FSCs located in as A-grade buildings, which typically also include green and non-green buildings. This study included high quality finishes and therefore command rela- a relatively small sample of South African FSCs tively high rental. There is a possibility that many A- located in green buildings. However, the sample of grade buildings do have some GBFIs (including FSCs was relatively complete as all the companies those related to IEQ), but the property owners renting space in South Africa’s largest REIT that choose not to opt for green certification, as this were in their green building portfolio and operating would be an additional cost that is not deemed in financial services were screened and only ten necessary. This could partially explain why there FSCs were found to have income, balanced and were individual occurrences of FSCs in the non- South African equity funds that were established at green buildings outperforming FSCs located in least five years ago. It should be further noted that green buildings, as the difference in the actual IEQ due to the small and exhausted sample size it is not of some A-grade and P-grade buildings is often not possible to determine and/or control for staff attrac- immediately noticeable. tion and retention (salaries and company culture) However, it must be noted that returns are sig- working for institutional versus boutique FSCs. nificantly impacted by a number of other external Another factor that is difficult to determine are and internal factors. External factors are characteris- other reasons (outside of protection against failing tics that are not in the control of asset manager(s), infrastructure) for FSCs to lease space in green certi- while internal factors are characteristics that are in fied office buildings. There could be a range of rea- the control of the asset manager(s) and the organ- sons, that may include corporate brand ization. Examples of external factors include unfore- enhancement, subtle recruiting strategy in the form seen socio-political and economic events that result a superior indoor working environment, and envir- in a sudden market shock (Prouska et al., 2016), such as COVID-19 where global economies were onmental consciousness as part of FSCs core com- reduced to only essential services during the height pany values, inter alia. JOURNAL OF SUSTAINABLE REAL ESTATE 55 Conclusion productivity. This data could be used to substantiate the findings of this research and forms part of the The study attempted to determine if there was any model (Figure 1) developed by (Nurick & Thatcher, benefits for FSCs located in a green building in terms 2021) to determine if there is a link between of organizational performance, as measured by annual- enhanced individual productivity and organizational ized return (Hypothesis 1 – partially accepted). There performance within the context of green office build- were indicationsthatFSCslocated in green buildings ings. Furthermore, this research could be replicated didon average yieldhigher annualizedreturns to FSCs in other markets where there is a larger sample of located in non-green buildings. The difference in per- green office buildings and FSCs that offer similar formance is not always immediately large but can lead funds, that may have different investment challenges to a large difference when allowed to compound over to that of South Africa. Additionally, a larger sample time, thus benefiting both the FSCs and their clients would allow for an investigation into FSC perform- over the long-term. It should be noted that extrapolat- ance in relation to company size (number of employ- ing future values is a hypothetical exercise, which is ees), and create the possibility of analyzing other often performed in industry by FSCs with the assump- funds that are commonly offered in developed finan- tion that current market conditions are relatively sta- cial markets, which are not widely offered to South ble/predictable over a long period of time. It could not African investors. A similar research approach could be conclusively established that there was a relation- also be applied to retail property in green buildings, ship between annualized return and IEQ scores where the financial performance of similar tenants (Hypothesis 2 – rejected), however there was an indi- (e.g., anchor tenants such as major supermarkets) cation that there is a moderate IEQ score range where could be compared to non-green certified retail annualized return seems to be maximized relative to buildings. There is also potential for the application the lower returns in the sample. Furthermore, the of similar type hypotheses within the context of light amount of time in a green building did not provide industrial property (i.e., distribution centres) to deter- any insight into the organizational performance of mine if an e-commerce type tenant performs better FSCs located in green buildings (Hypothesis 3 – in a green certified industrial building. rejected). A new metric (AGRRi) was used to normalize the IEQ score to determine if there was an incremental Acknowledgments difference in annualized return for each FSCs located The author wishes to acknowledge and thank Professor in a green building when compared to the average Andrew Thatcher at the University of the Witwatersrand for returns of the FSCs located in non-green buildings his assistance with this paper, specifically regarding the stat- (Hypothesis 4 – partially accepted). 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Journal

Journal of Sustainable Real EstateTaylor & Francis

Published: Dec 31, 2022

Keywords: Average green return ratio (AGRRi); financial services companies (FSCs); indoor environmental quality (IEQ); performance

References